CBE-LSE Bibliometric Analysis
Caleb Trujillo, Darcie Nelson, Rachel Kudlacz, Germaine Ng
2023-09-05
Introduction
There has been tremendous growth in the area of undergraduate biology research over the last fifteen years. This study attempts to summarize and analyze the progress of this growth for the journal of CBE-LSE.
Summary of initial data set
##
## Converting your wos collection into a bibliographic dataframe
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##
## Warning:
## In your file, some mandatory metadata are missing. Bibliometrix functions may not work properly!
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## Please, take a look at the vignettes:
## - 'Data Importing and Converting' (https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html)
## - 'A brief introduction to bibliometrix' (https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html)
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##
## Missing fields: DE
## Done!
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##
## Generating affiliation field tag AU_UN from C1: Done!After the results are processed, they are stored as data tables for information reporting.
## Description Results
## 1 MAIN INFORMATION ABOUT DATA
## 2 Timespan 2008:2022
## 3 Sources (Journals, Books, etc) 1
## 4 Documents 1072
## 5 Annual Growth Rate % - 1.83
## 6 Document Average Age 7.54
## 7 Average citations per doc 20.3
## 8 Average citations per year per doc 2.23
## 9 References 26262
## 10 DOCUMENT TYPES
## 11 article 920
## 12 bibliography 1
## 13 book review 1
## 14 correction 22
## 15 editorial material 90
## 16 letter 33
## 17 review 5
## 18 DOCUMENT CONTENTS
## 19 Keywords Plus (ID) 1302
## 20 Author's Keywords (DE) 0
## 21 AUTHORS
## 22 Authors 3187
## 23 Author Appearances 4746
## 24 Authors of single-authored docs 93
## 25 AUTHORS COLLABORATION
## 26 Single-authored docs 157
## 27 Documents per Author 0.336
## 28 Co-Authors per Doc 4.43
## 29 International co-authorships % 4.571
## 30Research Questions
Basic overview of the field
A timeline of the publication productivity is included for the journal.
Authoring
We focused on the most productive authors within the data set by H-index.
## Authors Articles Authors Articles Fractionalized
## 1 BROWNELL SE 36 TANNER KD 14.79
## 2 TANNER KD 34 DOLAN EL 12.34
## 3 DOLAN EL 28 ALLEN D 12.31
## 4 KNIGHT JK 26 EDDY SL 11.17
## 5 EDDY SL 21 STARK LA 10.29
## 6 SMITH MK 21 DOLAN E 9.00
## 7 COOPER KM 19 BROWNELL SE 8.57
## 8 ALLEN D 15 HOOPES LLM 7.03
## 9 HOOPES LLM 15 KNIGHT JK 6.94
## 10 STARK LA 15 GOUVEA JS 5.33We display a timeline of the productive authors.
##
## 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
## BROWNELL SE 0 0 0 0 1 0 3 2 3 7 3 2 7 5 3
## COOPER KM 0 0 0 0 0 0 0 0 1 3 1 1 4 6 3
## COUCH BA 0 0 0 0 0 0 0 2 3 3 1 2 1 2 0
## DOLAN EL 0 0 2 0 2 2 3 3 4 4 2 2 2 1 1
## EDDY SL 0 0 0 0 0 0 2 3 2 4 2 2 3 2 1
## FREEMAN S 0 0 1 1 0 0 2 0 0 1 1 2 0 0 0
## GRAHAM MJ 0 0 0 0 0 0 0 3 3 0 2 1 1 1 2
## HANAUER DI 0 0 0 0 1 0 2 2 2 0 2 0 0 1 2
## KNIGHT JK 1 0 2 2 0 2 0 3 3 2 3 4 3 1 0
## PRICE RM 0 0 0 0 1 2 3 0 0 2 1 0 0 1 1
## SMITH MK 1 0 1 3 0 2 2 1 2 1 2 3 2 1 0
## TANNER KD 1 2 1 3 4 3 1 3 0 4 2 2 4 3 1
## WENDEROTH MP 1 0 1 0 0 1 2 2 1 1 1 1 1 0 0
Institutions
Affiliations around the world
{r world corresponding author affiliations, echo=FALSE} # world_map = map_data("world") %>% # filter(long < 180) # # countries <- world_map %>% # distinct(region) %>% # rowid_to_column() # # allcountries <- data.frame(countries = countries$region, count = NA) # # for (country in countries$region) { # allcountries[countries == country] <- sum(str_count(data$C1,str_to_upper(country)),na.rm = TRUE) # } # # countryfreq <- results$Countries %>% # as.data.frame() %>% # mutate(Tab = str_to_title(Tab)) %>% # mutate(Tab = case_when( # Tab == "Usa" ~ "USA", # Tab == "United Kingdom" ~"UK", # TRUE ~ Tab # )) # # # # countryfreq <- allcountries %>% # filter(count > 0) %>% # mutate(region = countries) %>% # full_join(countryfreq,by = c("region" = "Tab")) %>% # mutate(coor_author_freq = Freq, Freq = count, Tab = region) # # world_map_short <- world_map %>% # full_join(countryfreq, by = c("region" = "Tab")) %>% # filter(Freq >= 1 & (subregion != "Alaska" | region != "New Zealand"))#%>% # #mutate(case_when(region == "USA", )) # # worldcentroids <- world_map_short %>% # group_by(region, Freq) %>% # filter(subregion != "Alaska" & subregion != "Pitt Island" & subregion != "Chatham Island" & subregion != "12"& subregion != "13") %>% # summarise(latitude = sign(lat) * exp(mean(log(abs(lat)))), longitude = sign(long) * exp(mean(log(abs(long))))) # # # AffiliationWorldMap <- countries %>% # full_join(countryfreq, by = c("region" = "region")) %>% # ggplot(aes(fill = Freq, map_id = region), color = 1) + # #geom_hline(yintercept=0, color = "light grey") + # geom_map(map = world_map_short, colour = "white", lwd = 0.2) + # geom_map(map = world_map, colour = "light grey", lwd = 0.2)+ # geom_point(data = worldcentroids, aes(x = longitude, y = latitude, size = Freq*100, group = region),color = "#333333") + # geom_text(data = worldcentroids, aes(x = longitude, y = latitude, label = Freq, size = Freq, group = region), color = "white") + # expand_limits(x = world_map_short$long, y = world_map_short$lat) + # coord_map("moll") + # #coord_quickmap()+ # #theme_map()+ # theme_default + # #labs(caption = "") + # theme(#plot.margin= unit(c(0.5, 0.5, 0.5, 0.5), "cm"), # # plot.caption = element_text(hjust = 0, face= "italic"), # axis.title = element_text(size = 14, color = "#555555"), # plot.title.position = "plot", # plot.caption.position = "plot", # legend.title = element_blank(), # #panel.background = element_rect(fill = "aliceblue", color = "aliceblue"), # legend.position = c(0.5, 0.01), # legend.background = element_rect(fill = NA), # legend.direction = "horizontal", # #legend.key.size = unit(0.4, "cm"), # legend.key.height= unit(.5, 'cm'), # legend.key.width= unit(.5, 'cm'), # panel.border = element_blank(), # panel.grid = element_blank(), # plot.background = element_blank() # #plot.caption.position = "plot", # #plot.caption = element_text(hjust = 0) # #legend.margin = margin(6, 12, 4, 1) # #legend.position = "none" # ) + # scale_fill_carto_c(palette = "BluGrn", direction = 1, # # trans = scales::pseudo_log_trans(sigma = 0.01), # # breaks = c(1,10, 100, 1000), # # labels = c(1,10, 100, 1000), # # limits = c(1,1000) # ) + # scale_size(range = c(3,14))+ # labs(fill = "Count of article", # title = "A. Published by country", # # x = NULL, # y = NULL # ) # # AffiliationWorldMap # # ggsave("figures/AffiliationWorldMapshort.jpg", device = "jpeg", dpi = 400, width = 7, # height = 3, # units = "in", limitsize = FALSE) #
Affiliations in the US
## # A tibble: 229 × 7
## ...1 AFF...2 AFF...3 Freq OPEID Issues ...7
## <dbl> <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 478 DIV EARTH AND LIFE SCI DIV EARTH AND LIFE SCI 1 na DC <NA>
## 2 501 FRANCE. FRANCE. 1 na ?? <NA>
## 3 531 INDIAN INST MANAGEMENT AHMEDABAD INDIAN INST MANAGEMENT AHMEDABAD 1 na Indian Institute of Manage… India
## 4 563 LIBERAL ARTS AND SCI ACAD LIBERAL ARTS AND SCI ACAD 1 na public magnet high school … <NA>
## 5 571 MARCO POLO TECHNOL MARCO POLO TECHNOL 1 na ?? <NA>
## 6 591 MT DESERT ISL BIOL LAB MT DESERT ISL BIOL LAB 1 na Mount Desert Island Biolog… Maine
## 7 594 NA NA 1 na ?? <NA>
## 8 620 OFF COMMUN AND NATL ACAD PRESS OFF COMMUN AND NATL ACAD PRESS 1 na ?? <NA>
## 9 625 OKLAHOMA MED RES FDN OKLAHOMA MED RES FDN 1 na Oklahoma Medical Research … <NA>
## 10 637 PRINCE EDWARD CTY HIGH SCH PRINCE EDWARD CTY HIGH SCH 1 na Prince Edward County High … Virg…
## # ℹ 219 more rows
## # A tibble: 7,904 × 4
## # Groups: eligibility, Institution Name [1,043]
## `Institution Name` name value eligibility
## <fct> <chr> <fct> <chr>
## 1 Auburn University AANAPISI 1 Ineligible
## 2 Auburn University AANAPISI F 1 Ineligible
## 3 Auburn University ANNH 1 Ineligible
## 4 Auburn University ANNH F 1 Ineligible
## 5 Auburn University HBCU 2 Ineligible
## 6 Auburn University HBCU Masters 2 Ineligible
## 7 Auburn University HBGI 2 Ineligible
## 8 Auburn University HSI 1 Ineligible
## 9 Auburn University HSI STEM 1 Ineligible
## 10 Auburn University NASNTI 1 Ineligible
## # ℹ 7,894 more rows## [,1]
## eligibility "Current grantee"
## titled "TRUE"
## AANAPISI NA
## AANAPISI F "10"
## ANNH NA
## ANNH F NA
## HBCU NA
## HBCU Masters "1"
## HBGI NA
## HSI NA
## HSI STEM "23"
## MSEIP "9"
## NASNTI NA
## NASNTI F NA
## PBI A "2"
## PBI F "3"
## PPOHA "4"
## SIP "23"
## TCCU NA## # A tibble: 9,112 × 4
## # Groups: Institution Name, eligibility [1,056]
## `Institution Name` name value eligibility
## <fct> <chr> <fct> <chr>
## 1 Auburn University AANAPISI 1 Ineligible
## 2 Auburn University AANAPISI F 1 Ineligible
## 3 Auburn University ANNH 1 Ineligible
## 4 Auburn University ANNH F 1 Ineligible
## 5 Auburn University HBCU 2 Ineligible
## 6 Auburn University HBCU Masters 2 Ineligible
## 7 Auburn University HBGI 2 Ineligible
## 8 Auburn University HSI 1 Ineligible
## 9 Auburn University HSI STEM 1 Ineligible
## 10 Auburn University NASNTI 1 Ineligible
## # ℹ 9,102 more rows## [1] 0.3967611## [1] 0.1477733##
## No Yes
## 1419 2016What are the collaborative connections between institutions?
Most cited papers and journals
Multiple Correspondence Analysis
## Tab
## STUDENT BIOLOG UNDERGRADU SCIENC RESEARCH LEARN TEACH EDUC EXPERI
## 361 336 236 222 216 186 167 118 92
## INTRODUCTORI DEVELOP STEM ASSESS PROGRAM MODEL IMPROV PRACTIC ANALYSI
## 89 85 83 73 58 54 53 53 51
## COURS COLLEG SCIENTIF CLASSROOM EFFECT CONCEPT FACULTI
## 51 50 50 49 49 48 47
